Time-restricted feeding promotes muscle function through purine cycle and AMPK signaling in Drosophila obesity models

Obesity caused by genetic and environmental factors can lead to compromised skeletal muscle function. Time-restricted feeding (TRF) has been shown to prevent muscle function decline from obesogenic challenges; however, its mechanism remains unclear. Here we demonstrate that TRF upregulates genes involved in glycine production (Sardh and CG5955) and utilization (Gnmt), while Dgat2, involved in triglyceride synthesis is downregulated in Drosophila models of diet- and genetic-induced obesity. Muscle-specific knockdown of Gnmt, Sardh, and CG5955 lead to muscle dysfunction, ectopic lipid accumulation, and loss of TRF-mediated benefits, while knockdown of Dgat2 retains muscle function during aging and reduces ectopic lipid accumulation. Further analyses demonstrate that TRF upregulates the purine cycle in a diet-induced obesity model and AMPK signaling-associated pathways in a genetic-induced obesity model. Overall, our data suggest that TRF improves muscle function through modulations of common and distinct pathways under different obesogenic challenges and provides potential targets for obesity treatments.

7. The authors find that AMPK phosphorylation is reduced in the model of sphinogosine kinase 2 mutants ( Figure 5). However, there is no difference in AMPK-phospho levels comparing TRF with ad libitum feeding. Hence, what have we learnt about the TRF protecting mechanism? It is trivial that knock-down of any of the essential glycolysis, TCA or ETC enzymes compromise flight activity. Again, there is no link to the feeding scheme provided.
Reviewer #3 (Remarks to the Author): Livelo et al. present a mechanistic approach to explain the benefits of time-restricted feeding (TRF) to skeletal muscle function in obesity using the Drosophila model organism. To this aim they apply a comparative circadian transcriptome analysis between skeletal muscle-enriched thorax material from wild type (WT) flies and diet-(DIO) or genetically-induced (GIO) fly obesity models under TRF or ad libitum fed (ALF) conditions. By in silico pathway analysis the authors identify three genes involved in glycine production/usage to be up-regulated under TRF in all three models. Tissue-specific knockdown of all three individual genes impairs flight performance, blunts the TRF benefits in this assay and promotes structural muscle degeneration and ectopic lipid accumulation in aging fly indirect flight muscle. Along these lines the authors identify fly Dgat2 as the only gene specifically down-regulated in all models under TRF vs. ALF. Flight muscle-specific knockdown of this gene improved flight performance and suppressed lipid accumulation under ALF in aging WT flies. In the following the authors use the same strategy to identify TRF-dependent transcriptional pathways specific for their HFD DIO and their Sk2 mutant GIO model, respectively. For the DIO model, they propose that TRF operates via purin biosynthesis and folate cycle to potentially improve muscle ATP availability. In support of this interpretation, they demonstrate that folic acid supplementation reverses some effects of HFD diet on physical performance and ATP levels in flies with genetically impaired folate cycle. Similarly, TRF up-regulates the AMPKalpha gene and a broad range of genes in AMPK downstream signaling pathways to improve ATP availability in the Sk2 mutant GIO model. In support of the functional implication of these transcriptional regulations, tissue-specific knockdown of some of these genes selectively impaired physiological performance in an age-dependent manner.
In general, this is an interesting study which targets the mechanism(s) of the evolutionarily conserved health benefits of TRF in obesity. The comparative transcriptome analysis followed by gene ontology and reactome analyses is straightforward and provides a wealth of data on candidate genes. Comparing the TRF-response of different genotypes and diet conditions is an elegant way to focus on important pathways. However, the functional data of this study are much less convincing due to a lack of metabolite analyses in support of the physiological consequences of the described gene regulations. Also, compensatory gene regulations by RNAi-mediated gene knockdown are poorly controlled leaving it largely open whether they recapitulate TRF-dependent physiological phenotypes or more fundamental impairments of the underlying metabolic pathways. The Dgat2 analysis lacks a compensatory gain-of-function approach.
This study would benefit from focusing on the more extended shared pathway analysis. Functional analysis of the individual DIO and GIO models is not very elaborate and suffers from the question how representative HFD and global Sk2 mutants are for DIO and GIO, respectively.
Major points: 1) Transcriptional fold-changes of ≥1.25 appear fairly subtle. In particular, compared to the impact of tissue-specific gene knockdown used for functional interventions. Flightless flies caused by some of the RNAi knockdowns suggest a strong overcompensation of the TRF-induced up-regulation of the genes of interest or even developmental effects. The authors have to take measures and demonstrate that the knockdown is timely and compensates but not overcompensates the TRF-induced up-regulation.
2) Does the glycine pool change as predicted in response to the transcriptional changes? 3) Several aspects of Fig. 2d-g are questionable. Sardh and CG5966 RNAi have very different impact on flight performance but muscle degeneration and ectopic lipid storage are comparable (and differ from the control). This challenges the view that we are looking at mechanistic rather than correlative changes. Moreover, how does a glycine pool reduction cause TAG accumulation? 4) Dgat2 is down-regulated under TRF. The authors need to demonstrate that TRF benefits (partially) vanish in response to compensatory Dgat2 overexpression adjusting the gene expression to Dgat2 levels under ALF. The functional Dgat2 data rely on a single RNAi construct (knockdown efficiency?), which is weak evidence. In particular, as Dgat2 is one of three paralogous genes of the Dgat2 family in flies. 5) Fig. 3d-f is not convincing. Size and abundance of the LDs appear too small for robust conclusions about possible changes. Also, these parameters need to be shown as scatter plots to more readily display the distributions. 6) Fig 2c, 3c: WT is an inappropriate control, which does not address driver line effects. Also, in 2c the very same control seems to be shown three times in all subpanels. Are they identical to the data shown in Fig. S4g? 7) FA supplementation is promising. However, how do transcriptional changes translate into changes in FA and FA-related metabolites? Direct measurements are necessary. 8) The flight index data of "Ctrl RNAi" in Fig. 4f and "Ctrl" in Fig. 5h are remarkable similar. Given that they are independent, why do "WT" flight indices vary so much between Fig. 2c or 3c and Fig. S6f? 9) The broad transcriptional up-regulation in GIO in response to TRF is interesting. However, how this translates to metabolic outcome is insufficiently addressed. The pAMPKalpha response is at the significance limit and the individual, strong knockdown of selected genes shown in Fig. 5h presents no conclusive picture. With other words without a complementary metabolite profile the GIO data remain preliminary. 10) Fig. S3b shows identical plots assigned to different genes. 11) Fig. S3c: "Ctrl" is "WT" according to text. 12) Transcriptional regulation of the central genes of interest derived from RNAseq data requires qRT PCR validation. 13) "… IFM-specific RNAi KD of Gnmt, Sardh and CG5955 resulted in a cytotoxic effect in the adipose tissue …" These data need to be presented (in the supplement).
Minor points: 14) The title sounds very general referring to DIO and GIO models, while a mild HFD diet and the Sk2 mutant are single models of unknown representative value for DIO and GIO. Given the wealth of transcriptome data the finding of "shared and unique pathways" sounds trivial. In particular, as it remains open if the unique pathways are unique for HFD diet and Sk2 mutants or for DIO and GIO. 15) Fig. 2f,g uses once CG5955 and once Tdh for the same gene. 16) Some SEMs are missing e.g. 18) The Actin88F-Gal4 driver needs more description concerning the developmental time-and tissuespecificity. 19) For RNAseq analysis, which samples were "ground to fine powder in liquid nitrogen" and which tissues were treated with the "Polytron homogenizer"? 20) The description of the p-AMPKalpha data is misleading as a single of the tested conditions is statistically significant. This is a very interesting study, providing more insight in the molecular mechanisms that may be responsible for the beneficial health effects of time-restricted feeding (TRF). Especially testing the functional importance of the genes identified by the transcriptomics by the muscle specific knock down and following flight assays, is a strong point of this study. Nevertheless, I have a couple of remarks: At several occasions the authors mention the importance of muscle tissue for glucose metabolism. However, in their Discussion it is not discussed whether the current results provide any insight in how TRF could improve glucose metabolism. At the beginning of their Introduction the authors mention 3 major contributors to the current obesity epidemic, genetic predisposition, calorie dense diets and chronodisruption. However, in their study they only use a DIO and GIO, but no CIO model. Although, I do not know whether chronodisruption also results in obesity in Drosophila, it would have been nice to discuss this aspect. Related to this, in rats and mice the TRF strategy has often been used as a model for chronodisruption by restricting food access to the normal sleep phase. Again I do not know whether this works in Drosophila, but it would be nice to know whether the changes in muscle tissue observed in the current study are opposite to those described in the rat and mice studies employing TRF during the sleep phase. I think at least the authors should mention/discuss this aspect. Figure S1 shows that more rhythmic genes were found during TRF than ALF in the WT and DIO groups, which is what was to be expected based on previous studies. However, in the GIO this pattern was reversed, i.e. more rhythmic genes in the ALF than in the TRF group. Misleading in this figure is that circle size is not representing the number of genes, in the WT and DIO groups the difference between ALF and TRF is approx. 100 genes, but in the GIO group this difference is >2000 genes! Typo's: Third line in the 3rd paragraph of the Introduction, "Recent" does not need a capital to start with.
First line in the final paragraph on page 5, "the accumulation" can be removed.
Second line on page 6, what is meant with the "cytotoxic effect in adipose tissue"?
Final lines of the 2nd paragraph on page 6, what is meant with "on aging"? Probably it should read "with aging"?

Reviewer #1 (Remarks to the authors):
This study identifies several genes related to the muscle functon(flight ability) of adult flies. The authors first performed a transcriptomic analysis of the indirect flight muscle with or without timerestricted feeding. They included two obesity models to give a broader view of the regulatory genes of muscle function. These conditions are based on the previous literature and therefore well established and characterised. The analysis identified Gnmt and many other genes that might be key to maintain muscle function under normal as well as time-restricted feeding.
While the study is obviously important, the authors did not address the deeper mechanistic analysis of their findings. Instead, it has listed up genes potentially related to the TRF-mediated improvement of muscle function. Also, they did not discriminate muscle homeostasis (during ageing) from development. Some additional experiments would help to improve the manuscript.

Major points:
1, In this study, the authors use Act88F-Gal4 to drive knockdowns of the genes of their interest. To my knowledge, Act88F is also expressed during development. If this is the case, it would be better to use Act88F with tub-Gal80ts or Act88F-GeneSwitch, which enables to manipulate only in the adult flies. Also, it would be better to show whether muscles are not affected in early young adults (such as day2~day5). GeneSwitch would also allow precise control of gene dosage, i.e. to block the gene upregulation by TRF. Strong knockdowns sometimes simply induce unhealthy organ development (of muscles). The authors also can test gain of function would result in the opposite phenotype to further strengthen the gene function.
Response: To address this important question raised by the reviewer, we have used two additional drivers for muscle-specific knockdown of Gnmt, Sardh, and CG5955: DJ694-Gal4 (Bryantsev et al 11675496) allowing precise timing of gene expression. We have examined the flight ability (muscle performance) of flies with IFM-specific KD of Gnmt, Sardh, and CG5955 using a DJ694 driver. No significant impairment of flight index was observed in 4-day-old flies, while a significant reduction in flight performance was observed at 3 weeks of age (Fig. 3a). Similarly, upon IFM-specific KD using Act88F-GS driver, flight performance was not affected on day 4 and reduced at 3 weeks (RU486 was supplemented on day 4 post eclosion) (Fig. S3f). These results suggest the crucial roles of Gnmt, Sardh, and CG5955 for muscle maintenance. We also performed IFM-specific KD of Dgat2 using DJ694 driver, and flight performance improvements were observed at 5 and 7 weeks (Fig. 4g).
We have acquired UAS-Gnmt, an overexpression fly stock from Dr. Masayuki Miura's lab (Obata et al, 2015, PMID: 26383889), and performed Gnmt overexpression using Act88F driver. Aligned with our hypothesis, we observed significant muscle improvement compared to age-matched control in 5week-old flies (Fig. S3g). Furthermore, we also acquired UAS-hDgat2 fly line from Bloomington Drosophila Stock Center (BDSC: 84854) and performed human Dgat2 overexpression using Act88F driver. Aligned with our hypothesis, we observed significantly reduced flight performance from 3-week-old flies (Fig. S4c). Overall, as suggested by reviewer 1, we have addressed the roles of Gnmt, Sardh and CG5955 on muscle function maintenance by performing gene KD during the adult stage. Additionally, we have evaluated the "gain-of-function" of Gnmt and hDgat2 in skeletal muscle, which supports our hypothesis.
2, Relatedly, the authors should always show, side-by-side, that whether the manipulation (of any genes) influences the muscle function in the normal condition or it abolishes TRF-mediated improvement. For example, in Fig. 2b-d, Gnmt was upregulated by TRF, and the knockdown abolished the change of the flight index by TRF. However, there is no control here. The data would be better to present as in Fig. S6c. Actually, the authors did not control the genetic background of the lines. The proper control might be required, especially when the basal Flight index can be different. For example, in Fig. S6c, Nmdmc-RNAi has higher Flight index at the basal line and this does not further increase by TRF. This would lead to a different conclusion.
Response: Thank you for pointing this out. We have added the proper control in Fig. 2b (now Fig.2h) side-by-side. While performing RNAi knockdown experiments, we have used two independent controls. 1) We crossed control RNAi lines with Act88F-Gal4 and used Act88F>Ctrl RNAi progeny as the control (plotted side-by-side with gene KD in the revised manuscript). 2) We also tested Act88F/+ (progeny of Act88F-Gal4 with W 1118 ), and similar flight indexes were observed between Act88F>Ctrl RNAi and Act88f/+ (Fig S3b). Flight indexes are now presented as an average of independent RNAi lines; for example, cohorts from two independent control RNAi lines are indicated as symbol circles or triangles. Flight index shows significant flight reduction upon knockdown of commonly upregulated genes, Fig. 3a) Flight performance upon muscle-specific KD of Gnmt, Sardh, and CG5955 using DJ694 driver. Fig S3f.) Flight performance upon muscle-specific KD of Gnmt, Sardh, and CG5955 using Act88F-GS driver. Fig. 4g) Flight performance upon IFM-specific KD of Dgat2 using DJ694 driver. Fig. S3g) Overexpression of Gnmt helps retain flight index in aging flies shown in 5-week-old female flies. Fig. S4c) Overexpression of hDgat2 led to impairment of flight index in 3-week-old flies.
demonstrating their role in muscle function, and is seen throughout 1, 3, and 5 weeks of age in female flies (Fig. 2d).
We have shown that KD of Gnmt, Sardh, and CG5955 using Act88F driver lost TRF mediated benefits, while TRF improved muscle performance in control flies at 3 and 5 weeks of age (Fig. 2h). For the flight index, each cohort of 10-20 flies is now individually plotted as a circle in all figures. Regarding figure S6c. there was an error made in plotting this figure. We attempted to compare flight ability upon knockdown of Gnmt and Nmdmc under HFD-ALF and HFD-TRF, however, we mistakenly plotted flight index from Act88F>Nmdmc-RNAi under standard diet. We apologize for this confusion. We have corrected Fig.S6c (now Fig.S7e), which contains all lines under a high-fat diet condition. The data now demonstrates that both Nmdmc and AdSL KD flies under HFD-TRF have no improvement in muscle performance compared to HFD-ALF.
3, While the transcriptional change is clear, the authors did not address the upstream pathways. How and which transcription factors are involved in up-or down-regulation of the genes by obesity and TRF?
Response: We appreciate the reviewer's comment regarding upstream pathways. Previous findings have found two possible upstream transcription factors which potentially modulate GNMT activities: cAMP- Fig. 2d) Flight performance of female flies with IFM-specific KD of Gnmt, Sardh or CG5955 using Act88F driver at 1, 3, and 5 weeks of age. Fig. 2h) Flight index showing loss of TRF-mediated benefits upon knockdown of common upregulated genes using Act88F-Gal4 driver in 3-and 5-week-old female flies. Fig. S3b) Flight index of Act88F/+ compared to Act88F> two independent control RNAi lines showed no difference. Fig. S7e) The corrected flight index of 3-week-old female flies with IFM-specific KD of Nmdmc and AdSL using Act88F driver under HFD-ALF and HFD-TRF. regulated transcription coactivator (CRTC) and Forkhead Box O (FOXO). We examined their gene expression from our transcriptome data. Interestingly, we found that Crtc expression levels were increased modestly under TRF (4 out of 6 time points) in WT and HFD flies, while FoxO expression level was significantly increased under TRF (5 out of 6 time points) in Sk2 flies (Fig. S3k). It has been shown that CRTC promotes gene expression associated with 1C metabolism pathway (and Gnmt/Sardh) and purine cycles leading to energy balance (Wang et al, 2021, PMID: 33723074). As we did not observe increases of purine cycle-related genes with the same magnitude in Sk2-TRF flies compared to HFD-TRF flies, one possibility is that CRTC promotes Gnmt expression under HFD-TRF, but not under Sk2-TRF. FOXO isoforms 1/3 have been found to play a role in regulating muscle energy homeostasis through control of glycolytic flux and mitochondrial metabolism. 4, The authors highlight several metabolic enzymes/pathways. However, unfortunately, no metabolic analysis has been done. It is possible that the gene expression change is due to the adaptation of metabolic alteration. So looking at mRNA level is usually not enough to discuss whether it is a cause or a consequence. In line with this, the authors should analyse whether IMP and related purine/THF Response: We thank the reviewer for this suggestion. We analyzed untargeted metabolites under ALF and TRF from IFMs in WT, HFD, and Sk2 flies by the Targeted Metabolomics Proteomics Laboratory (TMPL) at UAB. Metabolite analyses showed alteration of important metabolites related to the purine cycle, which support our transcriptomic data and functional data obtained with feeding folic acid. As shown in Fig. 5f, we found higher amounts of inosine, hypoxanthine, and xanthine under HFD-ALF than HFD-TRF. As inosine, hypoxanthine, and xanthine are products of ATP catabolism within the purine cycle (Farthing et al, 2015, PMID: 25956679), this may lead to a greater reduction of ATP levels under HFD-ALF.
Interestingly, under HFD-TRF, we found increased levels of fumaric acid, an intermediary product of the purine cycle. Fumaric acid can enter the TCA cycle and subsequently be converted to malic acid (also increased) which follows a path toward generating ATP. Overall, these metabolomic results support our hypothesis that TRF upregulated the purine cycle pathway, which generated more ATP compared to ALF in HFD flies.
We also analyzed metabolites in the Sk2 model in which we found TRF-mediated increases in NAD/NADH, which is necessary for TCA/ETC; Citrate/malic acid, acetylcarnitine, and L-carnitine, which are important for TCA cycle were also increased (Schroeder et al, 2012, PMID: 22238215). Furthermore, we found that melezitose and melibiose (di-and tri-saccharide) were both decreased in Sk2-TRF (Fig.  6g), suggesting activation of glycogenolysis. Analyses of additional metabolites are included in (Fig. S9-11). Though these results are in alignment with purine and AMPK involvement under TRF conditions, these results are limited in their ability to assess dynamic levels of metabolites. These results show only a snapshot rather than temporal changes which would require more sophisticated methods such as utilization of C13 labelling. Fig. 5f) Metabolites (under HFD-TRF versus HFD-ALF; Fold change ≥ 1.25) associated with purine cycle. Metabolites downregulated are associated with ATP breakdown products; upregulated metabolites are associated with ATP production through purine cycle and TCA. Fig. 6g) metabolites (under Sk2-TRF versus Sk2-ALF; Fold change ≥ 1.25) associated with AMPK signaling pathway. Decreased metabolites are di-and trisaccharides, which are potentially broken down in glycogen metabolism. Increased metabolites are related to TCA and ETC pathways. 5, It is not elucidated whether increased lipid in muscle is a cause of the phenotype in Fig. 2e-g. The authors may test Gnmt-, Sardh-, or CG5955-RNAi together with DGAT2-RNAi to see whether it rescues the decreased flight index.
Response: To the best of our knowledge, the interplay between Gnmt, Sardh, CG5955 and Dgat2 is currently unknown. We believe that it is likely that both lipid accumulation in muscle and ATP levels play a role in muscle performance. We performed Gnmt, Sardh or CG5955 KD along with Dgat2 KD, and no significant differences were observed in flight performance. This will need to be further studied in the future. Fig. 2b is not mentioned in the text.

6,
Response: Figure 2b has now been included in the text. 7, Fig. S4c,d in the text is actually Fig. S6c,d.
Response: Thank you, this has now been corrected and properly cited in the manuscript. 8, Is food intake affected by the gene manipulations, e.g. Gnmt-RNAi?
Response: We haven't noticed any differences in food intake with any RNAi driven by Act88F driver, compared to control. More importantly, IFM-specific knock-down of any genes was not associated with food intake.

Reviewer #2 (Remarks to the authors):
Livelo and colleagues are investigating how time restricted feeding (TRF) of different diets does impact muscle function during adult age in a Drosophila model. The authors feed flies for only 12h per day compared to ad libitum feeding and age them, while monitoring muscle function by performing a flight assay. How TRF positively affects organism health in general or individual organs in particular during ageing is an exciting field of study, which still lacks mechanistic insight, specifically in the muscle. To unveil a mechanism, the authors performed transcriptomics analysis of entire thoraces (not indirect flight muscles, as stated incorrectly in the abstract) and identified some metabolic enzymes displaying changed expression upon TRF on various diets or genetics models. Interestingly, knock-down of one of them, Dgat2, appears to result in TRF-like improvement of flight muscle function during ageing despite ad libitum feeding. In general, the paper appears to superficially follow various candidate genes that are not obviously related. Hence, it has an unclear title and a not very specific abstract. Many of the data presented appear preliminary of results are often over-interpreted. I am mainly commenting on the transcriptomics and genetic studies of the paper.
1. The authors used entire thoraces as source for the transcriptomics study. However, in the results and in the abstract the authors claim to have collected indirect flight muscles (IFMs). While the thorax is largely consisting of IFMs it contains various other muscles and digestive organs, hence this statement is incorrect.
Response: Thank you for pointing out this important issue. This error/inconsistency of using IFMs has been addressed for the transcriptomic study and other assays. We now have specified in Fig. 1b legend and method section that IFMs have been used in our transcriptomic study. In addition, IFMs were also used for qRT-PCR validation of TRF-regulated genes and metabolomic study.
2. The authors have isolated RNA samples every 4 hours over the course of one day, meaning 6 samples for each of the various feeding conditions. These samples were pooled to perform differential expression analysis using DE-Seq2, which requires replicates. The authors have not collected true replicates for any of the samples to verify the validity of this simplification procedure. As expression for many genes cycles during the day, this simplification is questionable. Similarly worrying is that the Suppl Table 1 is missing several of the samples entirely.Together with the low threshold of a FC 1.25 (not log2FC, which is standard) chosen by the authors, the data presented in Figure 1 are at least questionable and would require more detailed verifications for each presented gene. As only 8 genes were followed up, this verification would be doable by qPCR or tagged protein quantification.
Response: This is a reasonable question raised by the reviewer. We identified two samples that were outliers (see methods). Therefore, we removed them from downstream analysis and Supplementary Table 1. In the revised manuscript, the two outliers are indicated in Fig. S1a with a red dash box. The two outliers are now included in Supplementary Table 1  Here, the threshold of fold-change ≥1.25 was chosen in our manuscript to identify candidate genes regulated by TRF in both WT and obesity model flies. This threshold led to the identification of 8 candidate genes (5 up and 3 down), with most of the fold changes ≥ 1.5 (Fig. S2a). Within the 5 up genes, Gnmt, Sardh, and CG5955 appear to have function associations, sharing a role in glycine utilization and production. Altogether, the fold-change ≥1.25 threshold in our transcriptome data allowed us to identify generic TRF-mediated gene changes with functional association in WT and obesity model flies.
In agreement with the reviewer on further validation, we have performed qRT-PCR to validate the temporal expression of the 7 common DEGs (CG13992 has low expression and can not be detected robustly using qRT-PCR). As shown in Fig. S2b-c, increased mRNA levels of Gnmt, Sardh, CG5955, CG6806, and CG5896 were observed in at least 3 out of 4 time points in all TRF conditions, demonstrating similar trends as those seen in the RNA-seq data. (Fig. S2b for RNA seq; Fig.S2c for qRT-PCR). Dgat2 and CG7997 also showed similar trends, with most time points exhibiting reduced expression in all TRF conditions resembling the same trend seen in RNA-seq.
3. The authors test flight muscle function in a quite tedious version of the flight assay that can identify small differences between the different conditions following flight muscle specific gene knock-down ( Figure 2). This reviewer would like to see an individual results table to verify the statistics. However, neither the number of flies tested in the various conditions, nor their individual performance was provided in the Supplementary Tables. It is also unclear how often this experiment was repeated. It is well known that high GAL4 expression levels present in Act88F-GAL4 can impact flight. Was the 'wild type' control containing Act88F-GAL4? This is unclear from the figure legends or methods.
Response: In agreement with the reviewer, we have added more details regarding the flight muscle assay methodology and results in the method section and supplementary table of the manuscript. Previously, we plotted the mean flight indices per condition using the total # of flies with no indication of cohorts, N# of cohorts, total N#. This has now been changed to include each cohort fly muscle performance using 10-20 flies per cohort (shown as a circle, triangle or square if more than one RNAi line) and mean was calculated from all cohorts for each condition. All three parameters regarding the individual cohort performance, the number of cohorts and the number of total flies have been included in an individual results table (Source data). Moreover as indicated under reviewer 1 comment #2, while performing RNAi knockdown experiments, we have used two independent control RNAi lines crossed with Act88F-Gal4 and used Act88F>ctrl RNAi progeny as the control. Additionally, we have also tested Act88F/+, and similar flight index were observed between Act88F>Ctrl RNAi and Act88F/+ in female progeny ( Fig S3b).   Response: We thank the reviewer for their interest in this finding. As addressed in reviewer #3's 4 th comment, we have included two RNAi lines for Dgat2 knockdown using DJ694 driver. Dgat2 mRNA expression upon KD using Act88F and DJ694 driver are shown in Fig. S4a-b with ~75% and ~60% reduction respectively. We found muscle improvements induced by Dgat2 KD using both drivers were observed at week 5 and 7 of age ( Fig. 4c, g). Overall, we were able to support the important role of Dgat2 in skeletal muscle using two independent drivers (Act88F and DJ694) demonstrated by the protective effects on flight performance in old age (5 and 7 weeks). Additionally, we have employed an overexpression of human Dgat2 using Act88F driver under ALF and have shown impaired flight performance ( Fig. S4c) in addition to lipid increases in the first week of age ( Fig. S4d-f), indicating potential human translatability in addition to providing evidence for Dgat2's role in muscle performance.
We have also investigated the paralogs for Dgat2 (CG1941 and CG1946) with two independent RNAi line KDs using DJ694 driver. Their transcription levels remained unchanged under TRF, except CG1946 was upregulated under Sk2-TRF (Fig. S4g). Upon IFM-specific KD of CG1941 or CG1946 using DJ694 driver, muscle improvement was observed upon CG1941 KD at week 7 but wasn't as pronounced as the age-matched Dgat2 KD flies (Fig. S4h).
Left panel) Flight performance of 3-week-old female flies with IFM-specific KD of Gnmt, Sardh and CG5955 using Act88F driver under HFD-ALF and HFD-TRF. Right panel) Flight performance of 3-and 5-week-old female flies with IFMspecific KD of Gnmt, Sardh and CG5955 using Act88F driver under standard diet ALF and TRF.
6. Also Figure 4 is missing second RNAi lines for all of the genes shown.
Response: Second RNAi lines are now added to Fig. 4f (now Fig. 5e). The two RNAi lines' results were indicated by either circles or triangles. Similar flight performance from the two independent RNAi lines were observed for Gnmt and Nmdmc ( Fig.2d and 5e), therefore, we performed folic acid supplementation on IFM-specific knockdown using one RNAi line ( Fig. 5g-h).
7. The authors find that AMPK phosphorylation is reduced in the model of sphinogosine kinase 2 mutants ( Figure 5). However, there is no difference in AMPK-phospho levels comparing TRF with ad libitum feeding. Hence, what have we learnt about the TRF protecting mechanism? It is trivial that knock-down of any of the essential glycolysis, TCA or ETC enzymes compromise flight activity. Again, there is no link to the feeding scheme provided.
Response: We appreciate the comments. Our previous protein collection from different conditions was not controlled for time, we have re-collected protein samples from WT, HFD, and Sk2 ALF and TRF flies atfor one time point (ZT9). While AMPKα protein levels were unchanged under TRF compared to the ALF counterparts (Fig. S8i, j), increased p-AMPKα level was observed under Sk2-TRF versus Sk2-ALF (Fig. 6h, i, and Fig. S8h), which implies the potential upregulation of TRF-mediated AMPK activation, especially in Sk2 flies. To fully evaluate the protein levels of AMPKa under TRF in comparison to ALF, we collected thorax protein samples from Sk2 flies at ZT3, 9, 15, and 21 ( Fig. 6j, k). We found increased phosphorylated-AMPKα in Sk2-TRF compared to Sk2-ALF. Altogether, these results support our initial claim that TRF activates AMPK-associated pathways in Sk2 flies.
Our transcriptomic data showed that genes associated with glycolysis, glycogen metabolism, TCA, and ETC pathways were upregulated under Sk2-TRF. Given TRF results in muscle improvement, we first tested KD of these genes to validate their roles in muscle function. We found muscle function decline upon the KD of genes associated with these pathways (Fig. 6e, f). Therefore, increasing of glycolysis, glycogen metabolism, TCA, and ETC activity may account for at least a part of the beneficial effect of TRF. It is likely that not all enzymes within these pathways are essential for muscle functions. For example, in our previous study on Drosophila heart, we found TRF-mediated downregulation of genes associated with ETC. However, KD of 3 genes of ETC complex I components in heart showed cardioprotection, while KD of 1 gene showed no effect on heart function (Gill et al., 2015, PMID: 25766238). To further test the roles of these genes in Sk2-TRF mediated benefits, knockdown of mAcon1 or GlyS accompanied with Sk2 was made and we measured their flight performance under ALF and TRF (Fig. S8f). While TRF led to muscle improvements on mAcon1 KD flies, TRF failed to improve muscle performance on flies with mAcon1 and Sk2 double KD. Moreover, while GlyS KD flies didn't respond to TRF, GlyS and Sk2 double KD flies showed a further decline on muscle performance under TRF. Altogether, these results suggest that increasing basal activity of these AMPK associated pathways support TRF-mediated muscle benefits in Sk2 flies.

Reviewer #3 (Remarks to the authors):
Livelo et al. present a mechanistic approach to explain the benefits of time-restricted feeding (TRF) to skeletal muscle function in obesity using the Drosophila model organism. To this aim they apply a comparative circadian transcriptome analysis between skeletal muscle-enriched thorax material from wild type (WT) flies and diet-(DIO) or genetically-induced (GIO) fly obesity models under TRF or ad libitum fed (ALF) conditions. By in silico pathway analysis the authors identify three genes involved in glycine production/usage to be up-regulated under TRF in all three models. Tissue-specific knockdown of all three individual genes impairs flight performance, blunts the TRF benefits in this assay and promotes structural muscle degeneration and ectopic lipid accumulation in aging fly indirect flight muscle. Along these lines the authors identify fly Dgat2 as the only gene specifically down-regulated in all models under TRF vs. ALF. Flight muscle-specific knockdown of this gene improved flight performance and suppressed lipid accumulation under ALF in aging WT flies. In the following the authors use the same strategy to identify TRF-dependent transcriptional pathways specific for their HFD DIO and their Sk2 mutant GIO model, respectively. For the DIO model, they propose that TRF operates via purin biosynthesis and folate cycle to potentially improve muscle ATP availability. In support of this interpretation, they demonstrate that folic acid supplementation reverses some effects of HFD diet on physical performance and ATP levels in flies with genetically impaired folate cycle. Similarly, TRF up-regulates the AMPKalpha gene and a broad range of genes in AMPK downstream signaling pathways to improve ATP availability in the Sk2 mutant GIO model. In support of the functional implication of these transcriptional regulations, tissue-specific knockdown of some of these genes selectively impaired physiological performance in an age-dependent manner. In general, this is an interesting study which targets the mechanism(s) of the evolutionarily conserved health benefits of TRF in obesity. The comparative transcriptome analysis followed by gene ontology and reactome analyses is straightforward and provides a wealth of data on candidate genes. Comparing the TRF-response of different genotypes and diet conditions is an elegant way to focus on important pathways. However, the functional data of this study are much less convincing due to a lack of metabolite analyses in support of the physiological consequences of the described gene regulations. Also, compensatory gene regulations by RNAi-mediated gene knockdown are poorly controlled leaving it largely open whether they recapitulate TRF-dependent physiological phenotypes or more fundamental impairments of the underlying metabolic pathways. The Dgat2 analysis lacks a compensatory gain-of-function approach. This study would benefit from focusing on the more extended shared pathway analysis. Functional analysis of the individual DIO and GIO models is not very elaborate and suffers from the question how representative HFD and global Sk2 mutants are for DIO and GIO, respectively.

Major points:
1) Transcriptional fold-changes of ≥1.25 appear fairly subtle. In particular, compared to the impact of tissue-specific gene knockdown used for functional interventions. Flightless flies caused by some of the RNAi knockdowns suggest a strong overcompensation of the TRF-induced up-regulation of the genes of interest or even developmental effects. The authors have to take measures and demonstrate that the knockdown is timely and compensates but not overcompensates the TRF-induced up-regulation.
Response: As indicated from our response to reviewer 2 comment #2, in general, fold-change ≥1.25 and p ≤ 0.05 may not be used conventionally in most transcriptomics studies, however, TRF mediated fold changes are modest compared to other modulations, such as starvation, diet restriction, etc (see our previous study, Gill et al., 2015, PMID: 25766238, transcript showed fold change of similar magnitude between ALF and TRF from heart, head and periphery of 5-week-old flies.). Moreover, fold-change ≥1.25 has been used in multiple circadian studies and showed valuable findings (Fonseca Costa et al., 2017; Ma et al., 2021). Additionally, a recent human TRF study on skeletal muscle (Lundell et al., 2020, PMID: 32938935) also reported gene expression changes of similar magnitude. Here, the threshold of fold-change ≥1.25 was chosen in our manuscript to identify candidate genes regulated by TRF in both WT and obesity model flies. This threshold led to the identification of 8 candidate genes (5 up and 3 down) with most of the fold changes ≥ 1.5 (Fig. S2a). Within the 5 up genes, Gnmt, Sardh, and CG5955 appear to have functional association, sharing a role in glycine utilization and production. Altogether, the fold-change ≥1.25 threshold in our transcriptome data allowed us to identify generic TRF-mediated gene changes with a functional association in WT and obesity model flies.
We now have also incorporated IFM-specific knockdown with DJ694-Gal4 driver (Bryantsev et al, 2012, PMID: 22008792), which expresses only in adults within the indirect flight muscle post eclosion. We observed a less severe but still significant reduction in-flight performance at week 3 with DJ694 driver (Fig. 3a). We validated KD levels at week 3 using qRT-PCR and showed ~50% reduction in mRNA levels of Gnmt, Sardh, and CG5955 (Fig. S3d), which should not overcompensate the TRF-induced upregulation. Similarly, muscle-specific knockdown of Gnmt, Sardh, and CG5955 using DJ694 driver abolished TRF-mediated benefits on muscle performance (Fig. 3e).
2) Does the glycine pool change as predicted in response to the transcriptional changes?
Response: We have measured the glycine levels between WT and obesity models and between ALF and TRF using a glycine kit (Glycine is not detected through our metabolite analyses due to its chemistry). When comparing HFD-ALF to WT-ALF, there was a significant reduction in overall glycine levels (Fig. 2c). Referring to Fig. 2b, Sardh expression under HFD-ALF seems to be lower providing a potential explanation for this reduction in glycine level compared to WT and additionally, Gnmt was also higher potentially leading to greater utilization of glycine. We found that HFD-TRF only induced a nonsignificant increase in glycine levels compared to HFD-ALF, which may be due to activation of purine cycle, as purine cycle is known to consume glycine (Fig. 2c, and Diagram reference from Zhao et al, 2015, PMID: 25605736). When comparing Sk2-ALF to WT-ALF, we found a significantly increased amount of glycine ( Fig. 2c), which is likely because of increased levels of Sardh and CG5955 under Sk2-ALF versus WT-ALF (Fig. 2b). A further increase on glycine level was observed under TRF in Sk2 flies, which is likely due to the TRF-induced increases on Sardh and CG5955 levels (Fig. 2c). These increases in glycine levels in Sk2-TRF are likely needed in order for GNMT to regulate S-adenosylmethionine levels and subsequently lead towards AMPK signaling activation (Zubiete-Franco et al, 2016, PMID: 26394163). Fig. 2d) Flight performance upon muscle-specific KD of Gnmt, Sardh, and CG5955 using Act88F driver. Fig. 3a) Flight performance upon muscle-specific KD of Gnmt, Sardh, and CG5955 using DJ694 driver. Fig. 3e) Muscle-specific knockdown of Gnmt, Sardh and CG5955 using DJ694 driver abolished TRF-mediated benefits on muscle. 3) Several aspects of Fig. 2d-g are questionable. Sardh and CG5966 RNAi have very different impact on flight performance but muscle degeneration and ectopic lipid storage are comparable (and differ from the control). This challenges the view that we are looking at mechanistic rather than correlative changes. Moreover, how does a glycine pool reduction cause TAG accumulation?
Response: This is an important question raised by the reviewer. It is true that both KD in Sardh and CG5955 have impaired flight performance with CG5955 KD being the more severe of the two using Act88F-Gal4 driver (Fig. 2d) despite similar lipid deposition levels ( Fig. 2f and g). This disparity in flight impairment was not observed under the DJ694 driver between Sardh and CG5955 KD (Fig. 3a). This indicates a potential developmental effect associated with the CG5955 KD under Act88F-Gal4. Interestingly, CG5955 is known to function as a threonine dehydrogenase leading to the regulation of threonine levels, which subsequently produces glycine. Literature suggests that excess or absence of dietary threonine during development can reduce protein synthesis in skeletal muscle seen in pigs (Wang et al, 2007, PMID: 33723074). This may suggest that the developmental defect of CG5955 KD causes severe muscle impairment as seen in Fig. 2d. Developmental defects may also extend to lipid metabolism as lipid droplet density was higher in Act88F compared to DJ694 in CG5955 (Fig. 2e-g and Fig. 3b-d). CG5955's role in the literature extends towards having roles mediating the TCA cycle shown in a diagram reference (Tang et al, 2021, PMID: 34444752). This may suggest that early defects in the TCA cycle could lead to greater sustained impairments in lipid metabolism even throughout adulthood (Diagram reference from Tang et al, 2021 PMID: 34444752). Not much has been explored regarding this and will need further investigation to answer this question.
Regarding glycine levels and TAG accumulation, recent reports demonstrate that circulating glycine levels are associated with a favorable lipid and inflammatory plasma profile (higher HDLcholesterol and apolipoprotein A1, lower triglycerides, apolipoprotein B and C-reactive protein). Response: As responded for reviewer #2 comment #5, we now have two RNAi lines for Dgat2 knockdown using DJ694 driver. Dgat2 mRNA expression upon KD using Act88F and DJ694 driver are shown in Fig. S4a-b with ~75% and ~60% reduction respectively. Muscle improvements induced by Dgat2 KD using both drivers were observed at week 5 and 7 of age (Fig. 4c, g). We were able to support the important role of Dgat2 in skeletal muscle using two independent drivers (Act88F and DJ694) demonstrated by the protective effects on flight performance in old age (5 and 7 weeks). Additionally, we have employed an overexpression of human Dgat2 using Act88F driver under ALF and have shown impaired flight performance (Fig. S4c) in addition to lipid increases in the first week of age (Fig. S4d-f), indicating potential human translatability in addition to providing evidence for Dgat2's role in muscle performance.
The paralogs for Dgat2 (CG1941 and CG1946) now have been tested with two independent RNAi lines KD using DJ694 driver. Their transcription levels remained unchanged under TRF, except CG1946 was upregulated under Sk2-TRF (Fig. S4g). Upon IFM-specific KD of CG1941 or CG1946 using DJ694 driver, muscle improvement was observed upon CG1941 KD at week 7 but wasn't as pronounced as the age-matched Dgat2 KD flies (Fig. S4h).

Fig. 2d) Flight index of CG5955
KD compared with Sardh KD shows disparity even throughout 3 and 5 weeks of age under Act88F-Gal4. Fig. 3a) Flight index of CG5955 KD compared with Sardh KD shows similar flight indices compared to those observed in Act88F-Gal4 indicating a potential developmental associated effect. Fig. 2e) shows representative lipid staining with Nile red under Act88F-Gal4. Fig. 2f) shows relative lipid droplet size under Act88F-Gal4 in 3 weeks of age. Fig.  2g) Shows relative lipid droplet density under Act88F-Gal4 in 3 weeks of age. Fig. 3b) Shows representative lipid staining with Nile red under DJ694 driver. Fig. 3c) Shows relative lipid droplet size under DJ694 in 3 weeks of age with similar trends as seen in in Act88F driver. Fig. 3d) Shows notable changes in lipid droplet density under DJ694 in 3 weeks of age compared to Act88F suggesting developmental effects leading to impaired lipid metabolism causing higher density in Act88F driver. Diagram reference Tang et al, 2021, PMID: 34444752) Reference showing how "TDH" (CG5955 in Drosophila) leads to activation of TCA. Due to this role, developmental defects in CG5955 may lead towards impairment of lipid metabolism throughout adults. Fig. 3d-f is not convincing. Size and abundance of the LDs appear too small for robust conclusions about possible changes. Also, these parameters need to be shown as scatter plots to more readily display the distributions.

5)
Response: Thank you for this suggestion. Indeed, the lipid droplets are small at 3-week-old WT flies and Dgat2 KD flies. We have performed Dgat2 KD using both Act88F and DJ694 drivers, and lipid droplet area were reduced with both drivers (Fig. 4d-j), although no changes on lipid density (Fig. 4f, j). Abdomen lipid staining was also performed in fat body to ensure the Nile red quantification of lipids represent the true physiology (Fig. S5a-c). New representative images have been used for Fig 3d (now  Fig. 4d). Lipid droplet parameters are now shown as scatter plots. Fig 2c, 3c: WT is an inappropriate control, which does not address driver line effects. Also, in 2c the very same control seems to be shown three times in all subpanels. Are they identical to the data shown in Fig. S4g?

6)
Response: Thank you for pointing this out. While performing RNAi knockdown experiments, we have used two independent control RNAi lines crossed with Act88F-Gal4 and used Act88F>ctrl RNAi progeny as the control. Additionally, we have also tested Act88F/+, and similar flight index were observed between Act88F>Ctrl RNAi and Act88f/+ (Fig S3b). We have carefully revised the figures and ensured that controls are correctly described in figures and figure legends. 7) FA supplementation is promising. However, how do transcriptional changes translate into changes in FA and FA-related metabolites? Direct measurements are necessary.
Response: As also asked by reviewer 1, we appreciated the reviewer's suggestion on this. In our ongoing study we are analyzing untargeted metabolites under ALF and TRF from IFMs in WT, HFD and Sk2 flies, with the aid of the Targeted Metabolomics Proteomics Laboratory at UAB. Our metabolome analyses showed alteration of important metabolites related to purine cycle, which support our transcriptomic data as well as functional data obtained with feeding folic acid. As shown in Fig 5f, we found higher amounts of inosine, hypoxanthine and xanthine under HFD-ALF compared to HFD-TRF. As inosine, hypoxanthine and xanthine are products of ATP catabolism within the purine cycle (Farthing et al, 2015, PMID: 25956679) this may lead to greater reduction of ATP levels. Interestingly, under HFD-TRF, we found increased levels of fumaric acid, an intermediary product of the purine cycle. It is known that fumaric acid can enter in the TCA cycle and subsequently converted to malic acid (also increased) which follows a path towards generating ATP. Overall, these metabolomic results support our hypothesis that TRF upregulated purine biosynthesis pathway which generated more ATP compared to ALF in HFD flies. Though these results are in alignment with purine involvement under TRF conditions, these results are limited in their ability to assess dynamic levels of metabolites. These results show only a snapshot rather than temporal changes which would require more sophisticated methods such as utilization of C13 labelling.
8) The flight index data of "Ctrl RNAi" in Fig. 4f and "Ctrl" in Fig. 5h are remarkable similar. Given that they are independent, why do "WT" flight indices vary so much between Fig. 2c or 3c  6e-f). Parameters regarding the individual cohort performance, the number of cohorts, and the number of total flies have been included in an individual results table (Source Data). We thank the reviewer for this concern and would like to clarify that the control in Fig. 2c and 3c are Act88F> Ctrl RNAi flies, while Fig. S6f (now Fig. S7f) WT are Canton S flies. During data collection for flight index, the control group and experimental group were always handled by the same user within a close time period. Therefore, we believe our data have proper controls although there might be batch-to-batch variation.
9) The broad transcriptional up-regulation in GIO in response to TRF is interesting. However, how this translates to metabolic outcome is insufficiently addressed. The pAMPKalpha response is at the significance limit and the individual, strong knockdown of selected genes shown in Fig. 5h presents no conclusive picture. With other words without a complementary metabolite profile the GIO data remain preliminary.
Response: We appreciate this important question brought by the reviewer. We have collected untargeted metabolites under ALF and TRF from IFMs in Sk2 flies. We analyzed metabolites in the Sk2 model in which we found TRF-mediated increases in NAD/NADH, which is necessary for TCA/ETC; Citrate/Malic acid, acetylcarnitine, and L-carnitine, which are important for TCA (Schroeder et al, 2012, Circ Cadiovasc. Imaging). Furthermore, we found that melezitose and melibiose (di-and tri-saccharide) were both decreased in Sk2-TRF (Fig. 6g), suggesting activation of glycogenolysis.
As our previous protein collection from different conditions was not carefully controlled for time, we have re-collected protein samples from WT, HFD, and Sk2 ALF and TRF flies at the same time (ZT9). While AMPKα protein levels were unchanged under TRF compared to the ALF counterparts (Fig. S8i, j), increased p-AMPKα level was observed under Sk2-TRF versus Sk2-ALF (Fig. 6h, i, and Fig. S8h), which implies the potential upregulation of TRF-mediated AMPK activation especially in Sk2 flies. To fully evaluate the protein levels of AMPKa under TRF in comparison to ALF, we collected thorax protein samples from Sk2 flies at ZT3, 9, 15, and 21 (Fig. 6j, k). We found increased phosphorylated-AMPKα in Sk2-TRF compared to Sk2-ALF. Altogether, these results support our initial claim that TRF activate AMPK-associated pathways in Sk2 flies.
10) Fig. S3b shows identical plots assigned to different genes.
Response: Thank you for pointing this out, we have corrected this plot (Fig. S2b). Fig. S3c: "Ctrl" is "WT" according to text.

11)
Response: This has now been changed in the main text under results section and also seen in the figure legend for S3c 12) Transcriptional regulation of the central genes of interest derived from RNAseq data requires qRT PCR validation.
Response: qPCR validation has now been added ( Fig. S2c and S4b) 13) "… IFM-specific RNAi KD of Gnmt, Sardh and CG5955 resulted in a cytotoxic effect in the adipose tissue …" These data need to be presented (in the supplement).
Response: This was a typo and has been corrected to include lipid deposition and not "cytotoxic effects". We performed adipose tissue only to ensure that Nile red quantification of lipids represented the true physiology. We found no IFM-specific KD effects of these genes in the accumulation of lipid in the adipose tissue (Fig. S5) Minor points: 14) The title sounds very general referring to DIO and GIO models, while a mild HFD diet and the Sk2 mutant are single models of unknown representative value for DIO and GIO. Given the wealth of transcriptome data the finding of "shared and unique pathways" sounds trivial. In particular, as it remains open if the unique pathways are unique for HFD diet and Sk2 mutants or for DIO and GIO.  Fig. 6i) Ratios of p-AMPKα/ α-TUBULIN (normalized to trough of ALF value). Fig. 6j, k) p-AMPKα protein levels at ZT3, 9, 15, and 21 in Sk2-ALF and Sk2-TRF flies. Fig. 6g) Metabolites (fold change ≥ 1.25 under Sk2-TRF versus Sk2-ALF) associated with AMPK signaling pathway.
Response: In agreement with the reviewer, we have changed the title to "Time-restricted feeding attenuates muscle dysfunction through purine cycles and AMPK signaling in Drosophila obesity models" Previously, in the entire manuscript we have used the term "DIO" to refer to a high-fat diet-induced obesity model, however, as the representative value of our high-fat diet model is unknown we have changed this wording to "HFD". Also, the term "GIO" has also been changed to "Sk2" which was how we previously represented this model in our previous study (Villanueva et al, 2019, PMID: 31221967) 15) Fig. 2f,g uses once CG5955 and once Tdh for the same gene.
Response: "Tdh" has now been changed, this was the human name for the gene which should have been Cg5955. This has now been changed to keep the wording consistent. Response: Ampkα is the proper term and this has now been changed in addition to the correct spelling of "generate".
18) The Actin88F-Gal4 driver needs more description concerning the developmental time-and tissuespecificity.
Response: Along with the references, we have added more description regarding Actin88F-Gal4 and have additionally added 2 drivers: DJ694-Gal4 and Actin88F-geneswitch in order to address the developmental effects of using Actin88F-Gal4. Tissue specificity description has also been added for more clarity.
19) For RNAseq analysis, which samples were "ground to fine powder in liquid nitrogen" and which tissues were treated with the "Polytron homogenizer"?
Response: All tissues were treated with the polytron homogenizer. The method section has been corrected.
20) The description of the p-AMPKalpha data is misleading as a single of the tested conditions is statistically significant.
Response: As described for reviewer question # 9, we have re-run the assay with different time points and have included this in the figure (Fig. 6k, l). 21) Page 7: Supplementary Fig. 4c and 4d should read Supplementary Fig. 6c and 6d.
Response: Figure orders have been reworked and have been changed to ensure the correct citation of figures. Thank you for noticing this. Fig. S4c-e? Fat body? Muscle? The quality of Fig. S4c is low but does not support the quantifications in S4d,e as the density is clearly larger in Dgat2 RNAi while the LD area appears smaller.

22) "Abdomen lipid staining…" Which abdominal tissue has been analyzed in
Response: We have clarified that abdomen staining is done in the fat body (adipose tissue). New representative images have been used for Fig S4c-e (now Fig S5a-c).

23) Where are the RNAseq data deposited?
Response: The RNA seq data has submitted to Gene Expression Omnibus (GEO) on May 27, 2022, with he following secure token has been created to allow review of record GSE205334 while it remains in private status: Please use the following link to see the RNA seq data deposited to GEO using the token highlighted below. This is a very interesting study, providing more insight in the molecular mechanisms that may be responsible for the beneficial health effects of time-restricted feeding (TRF). Especially testing the functional importance of the genes identified by the transcriptomics by the muscle specific knock down and following flight assays, is a strong point of this study. Nevertheless, I have a couple of remarks: At several occasions the authors mention the importance of muscle tissue for glucose metabolism. However, in their Discussion it is not discussed whether the current results provide any insight in how TRF could improve glucose metabolism. At the beginning of their Introduction the authors mention 3 major contributors to the current obesity epidemic, genetic predisposition, calorie dense diets and chronodisruption. However, in their study they only use a DIO and GIO, but no CIO model. Although, I do not know whether chronodisruption also results in obesity in Drosophila, it would have been nice to discuss this aspect. Related to this, in rats and mice the TRF strategy has often been used as a model for chronodisruption by restricting food access to the normal sleep phase. Again I do not know whether this works in Drosophila, but it would be nice to know whether the changes in muscle tissue observed in the current study are opposite to those described in the rat and mice studies employing TRF during the sleep phase. I think at least the authors should mention/discuss this aspect.
Response: We greatly appreciate the positive feedback from this reviewer and also for bringing important remarks. In our previous study, we have evaluated the TRF-mediated effects on metabolic parameters including glucose metabolism and insulin resistance (Villanueva et al, 2019, PMID: 31221967). We agree with the importance of discussing glucose metabolism in TRF and have added more insight into the discussion section in paragraph #4. We also mentioned how the purine cycle, namely, AdSL is an insulin secretagogue, which may help mediate glucose uptake. Glucose metabolism may be uniquely regulated between HFD and Sk2 under TRF. For example, in Sk2-TRF, AMPKα showed increased expression levels and gene expression upregulation associated with AMPK downstream pathways such as glycolysis. This may suggest that under Sk2-TRF that there is greater glucose utilization through glycolysis and glycogen metabolism. These are some of the insights gained from this study that may help explain how glucose metabolism is improved under TRF in the skeletal muscle.
Also, Drosophila has been used as a model for chronodisruption, including results from our group shown in a previous study (Villanueva et al, 2019, PMID: 31221967). Previously we have demonstrated the effects of circadian disruption on muscle function with the induction of a light/light (LL) paradigm (wellestablished chronodisruption model). In addition to muscle dysfunction, we observed ectopic lipid deposition and insulin resistance upon chronodisruption, which was also observed in our obesity models (Villanueva et al, 2019, PMID: 31221967). More interestingly, imposing TRF resulted in attenuated muscle dysfunction, ectopic lipid accumulation, and insulin resistance in the chronodisruption model, similar to obesity models (Villanueva et al, 2019, PMID: 31221967). We are currently under a time-series experiment for collecting transcriptomic data during a 24 h cycle using the chronodisruption model under ALF and TRF. However, transcriptomic changes under TRF in the chronodisruption model are beyond the scope of this manuscript. Since our study is primarily focusing on obesity, we have reworded the manuscript text to discuss mainly obesity as a metabolic challenge. Figure S1 shows that more rhythmic genes were found during TRF than ALF in the WT and DIO groups, which is what was to be expected based on previous studies. However, in the GIO this pattern was reversed, i.e. more rhythmic genes in the ALF than in the TRF group. Misleading in this figure is that circle size is not representing the number of genes, in the WT and DIO groups the difference between ALF and TRF is approx. 100 genes, but in the GIO group this difference is >2000 genes! Response: In agreement with the reviewer, we have edited the figure to reflect the differences in order to prevent confusion with our venn diagram (Fig S1b). Third line in the 3rd paragraph of the Introduction, "Recent" does not need a capital to start with.

Typo's:
Response: This has now been corrected to lower case First line in the final paragraph on page 5, "the accumulation" can be removed.
Response: This has now been removed.
Second line on page 6, what is meant with the "cytotoxic effect in adipose tissue"?
Response: This was a typo, which has now been removed to include ectopic lipid deposition and myofibrillar disorganization instead.
Final lines of the 2nd paragraph on page 6, what is meant with "on aging"? Probably it should read "with aging"?
Response: This was a typo, "on aging" has also been removed and replaced.

Reviewer #1 (Remarks to the Author):
In the revised manuscript, the authors have addressed almost all the concerns that were raised from this reviewer and overall, their findings have been solidly confirmed. I can now recommend this study to publish. Only one minor comment: The authors have assessed the effect of folic acid feeding on flight performance and ATP levels. Although this experiment is not mandatory, one further investigation in how folate feeding affects other metabolites would strengthen their study.
Reviewer #2 (Remarks to the Author): I am happy to see that this revised manuscript by Livelo and colleagues has now an understandable title and abstract. The authors suggest that TRF in an aging fly obesity model results in some muscle function improvement by upregulation of genes producing glycine and SAM as well as downregulation of Dgat2, regulating triglyceride synthesis.
As explained in my initial review, I am not too surprised that drastic knock-down of glycine production enzymes Gnmt, Sardh and CG5955 result in a muscle phenotype, this might be interesting as explained by the authors, but the link to TRF rescue of obesity is unclear to me. This would require mild over-expression (1.25-fold?) and assay the effect under fatty diet. The authors did this only for one gene (Gnmt) and the effects seem small. Still the authors conclude that "Together, our results suggest that Gnmt, Sardh, and CG5955 are required for TRF-mediated improvement of skeletal muscle performance (line 269). This conclusion is not justified by the data presented.
Hence the effects seen upon knock-down of Dgat2, in the revised version attempted to be done specifically in adults, could be more interesting related to the mechanism of TRF rescued muscle deterioration.
The wealth of information presented in the manuscript comes with the cost that no gene or mechanism is investigated in detail. I still see many over interpretations of the data and it seems to me that unconclusive results are either ignored or mis-interpreted to fit the authors hypothesis. This is not the level of rigorousness I do expect for a Nat Comm article.
1. I appreciate that the authors have now attempted to verify the transcriptional changes by qPCR now shown in fig S2. In the text they state "validate these gene expression changes, which were found to be consistent". However, I do not see p-values in Figure S2c and it unclear to me why 4/4, 3/4 and 2/4 are shown? Does this mean in case of 2/4 only 2 out of 4 tests were plotted? If this is the case, I question the usefulness of data. Focusing on the important genes that are followed up here and plotting them properly with statistical analysis would be more useful and accurate. A general over-interpretation of the verification result in the text does not help.
2. I am also concerned that "outliers" in the transcriptomics analysis have been removed. What is the justification for this? Did the authors perform a PCA analysis that justified this measure?
3. It is interesting to see that the authors have now measured glycine levels in the thoraces of aged adults raised in the different conditions. However, they only find a small effect of TRF in the Sk2-/model, not in the wild type and not in high fat diet. Additionally, the Sk2-/-which supposedly mimics the HFD, has a totally different glycine level than the HFD flies. This questions to me many of the findings the authors present here or at least their interpretation that the protective mechanism of TRF is mediated through the modification of glycine levels by the slightly changed expression levels of the enzymes studied. Figure 2d-e presents only of one? And it is not specified which one. Obviously, testing 3 different ones means the results of each of these needs to be shown at least in one of the functional assays. Only these functional assays can rule out off-target effects (and not the assay testing knock-down efficiency of the on-target).

I appreciate that the authors have tested 3 independent RNAi lines in the Act88F induced RNAi experiments. However, the important
5. Line 250 "To our knowledge, we report for the first time the muscle-specific requirement of Gnmt, Sardh, and CG5955." A developmental flight muscle function for CG5955 had been reported in PMID 20220848. Developmental knock-down resulted in flightlessness.
6. Flight performance differences for Gnmt, Sardh, and CG5955 RNAi reported here with the supposedly adult specific DJ694 driver appear very minor to me, despite significance (Fig 3A). Is this biologically relevant? 7. line 282: "The expression levels of Dgat2 were reduced under TRF versus ALF at ≥ 4 time points from our transcriptomic data in WT and obesity models ( Fig. 4b and Supplementary Fig. 2b)." I do not see this reduction for wild type in Fig 4b. Were the assays done in Fig4d-j with flies raised on normal food of fatty food? If the first is the case and give the above not difference in wild type I question the usefulness of the finding.
8. In the response to the reviewers' comments the authors write that they now used gene-switch Act88F-GAL4 to induce knock-down in the adult only. It seems for the most interesting experiment, the adult specific knock-down of Dgat2, only the less characterized and likely not muscle specific DJ694-GAL4 line was used. Figures 5 and 6 were only done with developmental knock-down, so no need to comment from me in detail. The obvious developmental effects cannot be ruled out, see next point. 9. Nmdmc and AdSL were already described to be essential for normal muscle functon(PMID 20220848). This means both genes are essential for muscle function in general. Hence, it is trivial that a possible improvement induced by TRF does not happen upon knock-down of Nmdmc and AdSL. The same is also true for Ampkα (SNF1A), mAcon1, Ogdh (Nc73EF) and SdhD. These gene were all shown to be developmentally required for muscle functon(PMID 20220848).

Reviewer #3 (Remarks to the Author):
The authors present a carefully and comprehensively revised version of this interesting study, which satisfies almost all of my initial concerns.
In view of the remarkable extra experimental evidence added by the authors, it remains unclear, why they refraint from overexpressing human Dgat2 under TRF (and not under ALF) conditions to demonstrate that reverting the (endogenous) Dgat2 down-regulation erases the beneficial effects of TRF on flight performance.
The use of an additional, adult-specific driver for the indirect fly muscles is a real gain and strengthens the conclusion of the authors. Still, it is unclear to me, why the authors determined the knockdown efficiency under ALF and not under TRF to directly address the extent of the compensatory regulation.
"Abdomen lipid staining was also performed in fat body to ensure the Nile red quantification of lipids represent the true physiology (Fig. S5a-c)." The rationale of this argument is unclear to me as Act88F is introduced as IFM-specific driver. Showing that manipulation of any of the genes of interest in the flight muscles does not cause changes in the abdominal fat body LD population argues in favor of the absence of noncellautonomous effects. But it fail to prove the analytical value of the method as suggested by this statement. Any control causing a change in LD size/density is missing here. This needs to be corrected.
All in all I congratulate the authors to an insightful study, which broadens our understanding of the mechanisms underlying the beneficial effects of TRF.
Reviewer #4 (Remarks to the Author): I thank the authors for their answers and edits in response to my remarks. There´s only one remaining issue. In response to my remarks about Suppl Figure S1b the authors adapted the figure itself, but they did not respond to the question that was also in the remark, i.e. why is in the SK2 experiment the number of rhythmic genes (much) higher in the ALF group than in the TRF group? Which is contrary to what is expected and what is found in the WT and HF experiments.
In the revised manuscript, the authors have addressed almost all the concerns that were raised from this reviewer and overall, their findings have been solidly confirmed. I can now recommend this study to publish. Only one minor comment: The authors have assessed the effect of folic acid feeding on flight performance and ATP levels. Although this experiment is not mandatory, one further investigation in how folate feeding affects other metabolites would strengthen their study.
Response: Thank you for the encouraging words and for the insightful comment. We have not directly measured the effects of folic acid feeding on metabolites; however, we have shown increases in metabolites related to the purine cycle under HFD-TRF. Metabolites such as oxypurines (hypoxanthine, xanthine) were decreased in HFD-TRF compared to ALF. Another study also noted that decreased oxypurines were observed in folic acid-treated ischemic patients (PMID: 18362233). Furthermore, an insilico study predicted that folate depletion leads to a reduction of ATP pools (PMID: 32384607), while we observed increased ATP under HFD-TRF in our study. Taken together, although we have not directly assessed the effects of folic feeding on metabolites, we have some support indicating that HFD-TRF metabolites may likely reflect a similar metabolite output as folic feeding.
Reviewer #2 (Remarks to the Author): I am happy that this revised manuscript by Livelo and colleagues now has an understandable title and abstract. The authors suggest that TRF in an aging fly obesity model results in some muscle function improvement by upregulation of genes producing glycine and SAM as well as downregulation of Dgat2, regulating triglyceride synthesis.
As explained in my initial review, I am not too surprised that drastic knock-down of glycine production enzymes Gnmt, Sardh and CG5955 result in a muscle phenotype, this might be interesting as explained by the authors, but the link to TRF rescue of obesity is unclear to me. This would require mild overexpression (1.25-fold?) and assay the effect under fatty diet. The authors did this only for one gene (Gnmt) and the effects seem small. Still the authors conclude that "Together, our results suggest that Gnmt, Sardh, and CG5955 are required for TRF-mediated improvement of skeletal muscle performance (line 269). This conclusion is not justified by the data presented.
Response: Thank you for this comment. As suggested by the reviewer, we understand the concern that overexpression of Gnmt driven by Act88F driver may not fully recapitulate the physiological effects of TRF as the overexpression may be much higher than is actually observed under TRF conditions. To address this, we now have performed both Gnmt overexpression using the DJ694-Gal4 and Act88F-GS-Gal4 drivers with titrated doses of RU486 (no RU, 10nM RU, 50 nM RU and 100 nM RU) in both regular diets (RD) and under fatty diet (HFD). Relative expression levels of Gnmt are shown in Fig. 3f and Supplementary Fig. 3l. The levels of overexpression from either driver displayed expression levels of Gnmt similar to expression levels seen in TRF conditions while still displaying improvement in muscle performance in both RD and HFD ( Fig. 3g and Supplementary Fig. 3m, n). It is to note that we observed a mild reduction in flight performance from RU supplementation alone, therefore, we feel the DJ694 (Also expressed in the IFM, response for comment #9) driver would be a more amenable choice for most of the validation experiments. Altogether, our results support the idea that Gnmt overexpression benefits muscle performance/muscle maintenance when the expression is modulated to the levels seen in TRF. Transgenic overexpression stocks are not available for Sardh and CG5955, however, we found KD of all three genes Gnmt, Sardh, and CG5955 resulted in impairment of muscle function using the Act88F and DJ694 drivers. Further, loss of TRF beneficial effect on muscle performance was observed upon Gnmt, Sardh, and CG5955 KD driven by DJ694 (with 50-60% knockdown efficiency), suggesting their importance in TRF-mediated improvement of muscle function. As only Gnmt overexpression was tested, we have modified our statement to "Taken together, TRF-mediated upregulation of Gnmt, Sardh, and CG5955 may account for at least a part of the beneficial effect of TRF in skeletal muscle." Hence the effects seen upon knock-down of Dgat2, in the revised version attempted to be done specifically in adults, could be more interesting related to the mechanism of TRF rescued muscle deterioration. The wealth of information presented in the manuscript comes with the cost that no gene or mechanism is investigated in detail. I still see many over interpretations of the data and it seems to As we also addressed for reviewer 3, we conducted TRF upon Dgat2 KD in both RD and HFD. Interestingly, the 5-week-old flight performance was further improved under WT-TRF and HFD-TRF upon Dgat2 KD driven by DJ694 (Supplementary Fig. 4g), while additional reduction of Dgat2 expression levels was observed under TRF in the Dgat2 KD flies (Supplementary Fig. 4f). In addition, we overexpressed hDgat2 using DJ694 and improvements on muscle function were demonstrated (Supplementary Fig. 4i). It is noted that endogenous Dgat2 was reduced under TRF in hDgat2-OE flies ( Supplementary Fig. 4h). Altogether, our results suggested the possibility that TRF-mediated further reduction of Dgat2 may account for the observed muscle improvements from Dgat2 KD and Dgat2 OE flies under TRF. However, we are not able to preclude any other pleiotropic effects as a result of TRF which may be playing a contributing role in the observed muscle improvement. While we couldn't rule out if these additional improvements were from further lower levels of Dgat2 or other TRF-meditated changes, our results supported that TRF-mediated downregulation of Dgat2 is beneficial for muscle function.
See below as well as answers/justification to the additional specific questions/comments made by this reviewer. 1. I appreciate that the authors have now attempted to verify the transcriptional changes by qPCR now shown in fig S2. In the text they state "validate these gene expression changes, which were found to be  Figure S2c and it unclear to me why 4/4, 3/4 and 2/4 are shown? Does this mean in case of 2/4 only 2 out of 4 tests were plotted? If this is the case, I question the usefulness of data. Focusing on the important genes that are followed up here and plotting them properly with statistical analysis would be more useful and accurate. A general over-interpretation of the verification result in the text does not help.
Response: We appreciate the suggestion for the usage of p-values and now have incorporated this into Fig. S2c. The numbers "4/4" for example indicate the number of time points where gene expression was increased or decreased under TRF compared to ALF. This was shown as a way to display upregulated or downregulated gene time-series expression patterns in sequencing and qPCR data under ALF and TRF. In order to avoid potential confusion, we have now removed the numbers in the revised manuscript.
2. I am also concerned that "outliers" in the transcriptomics analysis have been removed. What is the justification for this? Did the authors perform a PCA analysis that justified this measure?
Response: Yes, PCA analysis was included in the previously revised version (Fig. S1a). Outliers are indicated with a dashed red box, the figure has been added below as a reference. 3. It is interesting to see that the authors have now measured glycine levels in the thoraces of aged adults raised in the different conditions. However, they only find a small effect of TRF in the Sk2-/model, not in the wild type and not in high fat diet. Additionally, the Sk2-/-which supposedly mimics the HFD, has a totally different glycine level than the HFD flies. This questions to me many of the findings the authors present here or at least their interpretation that the protective mechanism of TRF is mediated through the modification of glycine levels by the slightly changed expression levels of the enzymes studied.
Response: Though we did not measure a significant increase in glycine levels under HFD-TRF despite the upregulation of glycine-producing genes Sardh and CG5955, we hypothesized that this could be due to the TRF-mediated activation of the purine cycle. Activation of the purine cycle leads to the consumption of glycine in a critical step mediated by phosphoribosylglycinamide transformylase (Gart) potentially countering the increase of glycine production. In WT and Sk2 flies, we did not observe pronounced activation of the genes associated with the purine cycle ( Fig. 5 and Supplementary Fig. 7a) To test our hypothesis about purine cycle upregulation countering the effects of glycine production in HFD-TRF, we performed Gart KD and subjected those flies to HFD-TRF. Without the Gart KD, we found similar results where glycine was not increased under TRF in HFD. Upon Gart KD, glycine was found to be upregulated in HFD-TRF compared to HFD-ALF ( Supplementary Fig. 7c). Moreover, Nmdmc, a necessary gene shown to be critical for purine cycle activation (PMID: 26912861) was found to be significantly increased under TRF in HFD but not Sk2 (Supplementary Fig.7a). This supports the idea that purine cycles are activated under TRF mainly in HFD, but not Sk2. Regarding Sk2, Gart levels are relatively high, however, no significant differences are observed between Sk2-ALF and TRF ( Supplementary Fig.7a). In conjunction with having lower levels of Nmdmc expression, a critical gene for encoding a key enzyme in purine activation, this suggests that increases in glycine levels found in Sk2 did not undergo the same masking effect as shown in HFD-TRF as a result of purine cycle activation. It is to note, however, in an attempt to show that Gart had no effects on Sk2 TRF vs. ALF, we conducted simultaneous KD of Gart2 and Sk2 in the IFM using Act88F and found that TRF in Sk2/Gart KD no longer had increased glycine shown in Supplementary Fig. 7c. This is potentially because only ~ 20% KD of Sk2 was achieved with the Act88F driver when combined with Gart KD, which might be not sufficient to alter glycine levels. Due to this limitation, we are not able to certainly conclude the independence of Sk2 TRF from Gart. Regarding the differences in glycine levels between HFD and Sk2, it will be difficult to assess the differences in glycine levels as different pathways of obesity are involved. For example, having higher glycine levels in Sk2 may be a compensatory effect to help regulate genetic-induced metabolic dysfunction which may or may not occur in HFD conditions. From our data, we were at least able to observe that TRF leads to an increase in glycine level in both HFD with Gart KD and Sk2 mutants. Further, as glycine levels were originally found to be similar in ALF/TRF in HFD, this suggests that increased glycine resulting from TRF is used towards the activation of the purine cycle as upon introducing KD of Gart, glycine levels were then found to be significantly increased in HFD-TRF. Fig. S7a) Log expression of genes involved in the purine cycle found to be increased in TRF under HFD conditions. Shown here, Gart is significantly increased under TRF in HFD compared to ALF and Nmdmc is relatively low in both ALF/TRF in Sk2. Fig. S7c) Relative glycine levels upon Gart2 KD under HFD-TRF. Figure R2. Relative Sardh and CG5955 expression levels in CS, Sk2, Act88F>Ctrl RNAi and Act88F>Sk2 RNAi/Ctrl RNAi.
4. I appreciate that the authors have tested 3 independent RNAi lines in the Act88F induced RNAi experiments. However, the important Figure 2d-e presents only of one? And it is not specified which one. Obviously, testing 3 different ones means the results of each of these needs to be shown at least in one of the functional assays. Only these functional assays can rule out off-target effects (and not the assay testing knock-down efficiency of the on-target).
Response: We tested 3 independent RNAi lines on flight performance (Fig. 2d) and results from independent RNAi lines were indicated with the symbol circle, triangle, or square, which has been described in the figure legend. We understand that combining data points from 3 independent lines might mask the shape differences, therefore, we have added supplementary Fig. 3c to show results from the 3 independent lines separately. The RNAi lines used in Fig. 2d-e have been specified in the source data file and now they are also specified in the method section. 5. Line 250 "To our knowledge, we report for the first time the muscle-specific requirement of Gnmt, Sardh, and CG5955." A developmental flight muscle function for CG5955 had been reported in PMID 20220848. Developmental knock-down resulted in flightlessness.
Response: Thank you for pointing this out. We have reworded this statement to indicate that Gnmt, Sardh and CG5955 are required for muscle maintenance using DJ694 driver (Fig. S3f) in addition to validating their roles during muscle development via Act88F driver (Fig. 2d). The statement has been modified as "… suggesting the important roles of Gnmt, Sardh, and CG5955 in muscle function and maintenance". It is to note that a Mef2-Gal4 driver was used in PMID 20220848 which is not an IFMspecific driver but a driver for all muscle cell lineages including the heart, body wall, and other muscles.
6. Flight performance differences for Gnmt, Sardh, and CG5955 RNAi reported here with the supposedly adult specific DJ694 driver appear very minor to me, despite significance (Fig 3A). Is this biologically relevant? Response: Overall, it was observed that ~11% reduction in muscle performance resulted from adult IFMspecific KD in Gnmt, Sardh and CG5955 in 3-week-old female flies. In measuring flight performance, this magnitude of reduction in muscle performance is clearly observed and mimics muscle performance seen in older adult flies. In humans, considering that muscle strength declines around 1.5% between age 50-60 and by 3% thereafter (PMID: 23160774) 11% reduction in muscle performance seems to fall within biological relevance. 7. line 282: "The expression levels of Dgat2 were reduced under TRF versus ALF at ≥ 4 time points from our transcriptomic data in WT and obesity models ( Fig. 4b and Supplementary Fig. 2b)." I do not see this reduction for wild type in Fig 4b. Were the assays done in Fig4d-j with flies raised on normal food of fatty food? If the first is the case and give the above not difference in wild type I question the usefulness of the finding.
Response: We have now replotted expression levels without the 2 outliers and their corresponding ALF/TRF counterparts, which represents more appropriate comparisons of expression levels ( Fig. 4b and Supplementary Fig. 2b). When comparing Dgat2 expression under ALF and TRF, the comparisons were made at each specific time point. We understand that Dgat2 expression levels at certain time points are close between ALF and TRF, therefore, we have now modified the statement that "The expression levels of Dgat2 were downregulated under TRF versus ALF from our transcriptomic data in WT and obesity models ( Fig. 4b and Supplementary Fig. 2b)." As DESeq analysis showed Dgat2 expression levels were reduced under TRF in all WT, HFD and Sk2 flies (Fig. 4b), the majority of assays were done under regular food. We agree that performing the assays under HFD would provide valuable information. We now have examined the flight performance of 5-week-old female flies under TRF upon Dgat2 KD in both RD and HFD (Supplementary Fig 4g). Consistent with the observation in RD, DJ694-driven KD of Dgat2 improved flight performance in HFD under TRF.  Figure S4g. Flight performance of 5-week-old female flies upon DJ694 driven Dgat2 KD under ALF and TRF in RD or HFD.
Response: The literary source cited a study that used the Mef2-Gal4 driver for inducing RNAi knockdown of genes, of which Nmdmc, AdSL and Ampkα related genes were found to be essential for muscle function (also cited in the revised manuscript). It is to note that Mef-2 is a driver for multiple muscle lineages and is not IFM-specific. When we performed KD using the Act88F driver, our results aligned with the idea that these genes were developmentally required for muscle function. As the focus of this manuscript is on the mechanism of TRF's effect on IFM, we performed IFM-specific knockdown using DJ694. Muscle function wasn't severely affected by the KD of those genes using the DJ694 driver (except Ogdh). However, TRF failed to improve muscle performance (except in mAcon1 KD flies), suggesting the potential involvement of Nmdmc, AdSL, Ampkα, Ogdh, and SdhD for TRF beneficial effects on muscle.
Reviewer #3 (Remarks to the Author): The authors present a carefully and comprehensively revised version of this interesting study, which satisfies almost all of my initial concerns.
In view of the remarkable extra experimental evidence added by the authors, it remains unclear, why they refraint from overexpressing human Dgat2 under TRF (and not under ALF) conditions to demonstrate that reverting the (endogenous) Dgat2 down-regulation erases the beneficial effects of TRF on flight performance.
Response: Thank you for the comment. We now have performed TRF on flies with hDgat2 overexpression driven by DJ694 in both RD and HFD. Interestingly, improved flight performance was still observed under TRF compared to age-matched ALF ( Supplementary Fig. 4i). It is not completely surprising as Dgat2 is not the only gene modulated under TRF, and reduction of endogenous Dgat2 from TRF may counter the effects of hDgat2 overexpression. The possibility of hDagt2 overexpression not disturbing other TRF-modulated changes, and those changes leading to improvement of muscle performance during aging or obesogenic challenges, however, cannot be ruled out. Although it will be an interesting topic to decipher the extent of Dgat2 modulation or other modulations on TRF beneficial effects, we feel that it is not within the scope of this manuscript. The use of an additional, adult-specific driver for the indirect fly muscles is a real gain and strengthens the conclusion of the authors. Still, it is unclear to me, why the authors determined the knockdown efficiency under ALF and not under TRF to directly address the extent of the compensatory regulation.
Response: To address the extent of the compensatory regulation, we performed knockdown using adultspecific muscle driver DJ694. Our qPCR validation has shown ~40-60% knockdown level for Gnmt, Sardh, and CG5955. While mild increases in expression levels of Gnmt, Sardh, and CG5955 were observed under TRF in their corresponding KD flies (Fig. S3g), no muscle improvements were observed.
"Abdomen lipid staining was also performed in fat body to ensure the Nile red quantification of lipids represent the true physiology (Fig. S5a-c). The rationale of this argument is unclear to me as Act88F is introduced as an IFM-specific driver. Showing that manipulation of any of the genes of interest in the flight muscles does not cause changes in the abdominal fat body LD population argues in favor of the absence of non-cell autonomous effects. But it fail to prove the analytical value of the method as suggested by this statement. Any control causing a change in LD size/density is missing here. This needs to be corrected. All in all, I congratulate the authors to an insightful study, which broadens our understanding of the mechanisms underlying the beneficial effects of TRF.
Response: Thank you for the encouragement and the comment. The lipid staining and imaging method have been applied in our previous publication (PMID: 31221967) where we demonstrated increased numbers of lipid droplets in IFMs of HFD and Sk2 flies compared to WT flies. We have imaged HFD as a positive control along with the experimental flies (Genotype: Act88F>Ctrl RNAi). Increased numbers of bigger lipid droplets were shown in Fig. R3. We have now modified the statement to "Abdomen lipid staining was also performed in the fat body and no significant differences were observed ( Supplementary Fig. 5a-c)."

Fig. R1
Reviewer #4 (Remarks to the Author): I thank the authors for their answers and edits in response to my remarks. There´s only one remaining issue. In response to my remarks about Suppl Figure S1b the authors adapted the figure itself, but they did not respond to the question that was also in the remark, i.e. why is in the SK2 experiment the number of rhythmic genes (much) higher in the ALF group than in the TRF group? Which is contrary to what is expected and what is found in the WT and HF experiments.
Response: Thank you for the comment. We are aware that TRF is known to increase the number of rhythmic genes in most of the independent TRF studies on varied tissues under aging or high-fat diet conditions. One possible explanation for fewer rhythmic genes in the TRF group compared to the ALF group is that the Sk2 mutant fly is a genetic-induced obesity model, which might have more rhythmic genes. Moreover, TRF induces beneficial effects that are pleiotropic, and not limited to maintaining expression rhythmicity. One recent study has shown similar observations with fewer rhythmic genes under TRF compared to ALF upon knock-out of Clk in a mice model (PMID: 30174302). At this stage, we did not explore why fewer rhythmic genes were found under TRF in Sk2 mutants, we feel that a detailed delineation of the underlying mechanisms would be important but beyond the scope of the current study. This explanation has been added to the manuscript text under results as well. I appreciate that the authors have further improved their manuscript according to some of my earlier comments. I just want to come back to my point former point 9 that stated: "Nmdmc and AdSL were already described to be essential for normal muscle functon(PMID 20220848). This means both genes are essential for muscle function in general. Hence, it is trivial that a possible improvement induced by TRF does not happen upon knock-down of Nmdmc and AdSL. The same is also true for Ampkα (SNF1A), mAcon1, Ogdh (Nc73EF) and SdhD. These gene were all shown to be developmentally required for muscle functon(PMID 20220848)." Response: The literary source cited a study that used the Mef2-Gal4 driver for inducing RNAi knockdown of genes, of which Nmdmc, AdSL and Ampkα related genes were found to be essential for muscle functon(also cited in the revised manuscript). It is to note that Mef-2 is a driver for multiple muscle lineages and is not IFM-specific. When we performed KD using the Act88F driver, our results aligned with the idea that these genes were developmentally required for muscle function. As the focus of this manuscript is on the mechanism of TRF's effect on IFM, we performed IFM-specific knockdown using DJ694. Muscle function wasn't severely affected by the KD of those genes using the DJ694 driver (except Ogdh). However, TRF failed to improve muscle performance (except in mAcon1 KD flies), suggesting the potential involvement of Nmdmc, AdSL, Ampkα, Ogdh, and SdhD for TRF beneficial effects on muscle.
I have looked at the initial reference of the here used DJ694 driver (PMID: 12882353). This paper shows that DJ694 is NOT IFM-specific (in contrast to Act88F), there is expression in many cells in the head and in the abdomen. Furthermore, this initial reference does NOT show that this driver is off in the developing IFMs.
Despite the statement above, this revised manuscript does NOT cite PMID 20220848.
Reviewer #3 (Remarks to the Author): My remaining concerns have been satisfingly addressed and I congratulate the authors to a carefully revised and interesting study.
Reviewer #4 (Remarks to the Author): I have no further comments.

REVIEWERS' COMMENTS
Reviewer #2 (Remarks to the Author): I appreciate that the authors have further improved their manuscript according to some of my earlier comments. I just want to come back to my point former point 9 that stated: "Nmdmc and AdSL were already described to be essential for normal muscle functon(PMID 20220848). This means both genes are essential for muscle function in general. Hence, it is trivial that a possible improvement induced by TRF does not happen upon knock-down of Nmdmc and AdSL. The same is also true for Ampkα (SNF1A), mAcon1, Ogdh (Nc73EF) and SdhD. These gene were all shown to be developmentally required for muscle functon(PMID 20220848)." I have looked at the initial reference of the here used DJ694 driver (PMID: 12882353). This paper shows that DJ694 is NOT IFM-specific (in contrast to Act88F), there is expression in many cells in the head and in the abdomen. Furthermore, this initial reference does NOT show that this driver is off in the developing IFMs. Despite the statement above, this revised manuscript does NOT cite PMID 20220848.
Response: We sincerely apologize for not including PMID 20220848 in the revised manuscript, and the manuscript has now been added to the reference. We agree with the reviewer that, in principle, the loss of TRF-mediated benefit upon knockdown of Nmdmc, AdSL, Ampkα (SNF1A), mAcon1, Ogdh (Nc73EF), and SdhD, could be potentially due to the knockdown of genes essential for muscle function but not necessarily due to the absence of upregulation induced by TRF. However, the results from the abovementioned experiments, together with our transcriptomic and metabolomics data, complementarily support our main finding of this manuscript that TRF improves muscle function through modulation of purine cycle (Nmdmc, AdSL) in HFD, and Ampk signaling (Ampkα (SNF1A)), glycogen metabolism, glycolysis, TCA (mAcon1, Ogdh (Nc73EF)) and ETC (SdhD) in Sk2. Our transcriptomic data showed significant upregulation of genes involved in the purine cycle under HFD-TRF but not Sk2-TRF, while significant upregulation of genes involved in AMPK-associated pathways in Sk2-TRF but not HFD-TRF. Moreover, the metabolomic changes also aligned with the upregulation of distinct pathways in two obese models.
Upon further evaluation of the PMID: 12882353 article on the DJ694 driver, DJ694 is a muscle-specific driver that expresses thoracic flight muscle and abdominal muscle, as well as some cells in the head. PMID: 12882353 demonstrated that the expression level of the DJ694 driver increases rapidly upon eclosion (Fig 3d in PMID: 12882353), and in the longitudinal flight muscle, the DJ694 reporter increases monotonically across all ages (Fig 5a in PMID: 12882353). Although DJ694 expresses during the larval and pupal phase, it is not expressed in the larval and pupal muscles but instead in the oenocytes and salivary glands (Figure 2 in PMID: 29259847). Another study also indicated that the DJ694-Gal4 driver becomes initiated in adults, especially in IFMs, upon eclosion (PMID: 22008792). They utilized DJ694 to examine the role of MEF2 in the adult IFM (PMID: 22008792). Therefore, the goal of modestly modulating candidate genes in the adult stage would be greatly facilitated by using the DJ694 driver because of the absence of expression in muscle before the adult stage.
In our study, we observed modest, or, no significant differences in flight performance upon DJ694driven KD of Gnmt, Sardh and CG5955 at day 4 ( Figure 3a and Supplementary Figure 3f). In addition, DJ694-driven KD of AdSL, Nmdmc, Ampkα and SdhD didn't lead to any significant decline in flight performance at the age of week 5 ( Supplementary Figure 7f and 8h).
We acknowledge that DJ694-driven KD also occurs at oenocytes and salivary glands during the developmental phase and at abdominal muscle during the adult phase. We now have carefully stated our rationale for choosing the DJ694 driver in our study and noted in the discussion that the potential effects on flight performance from DJ694-driven KD occurring outside of IFM are not assessed by our methods.
In the "results" section: In order to examine the role of Gnmt, Sardh and CG5955 in IFM during the adult phase, we utilized DJ694-Gal4 driver, an adult-muscle driver that initiates upon eclosion and remains active during the whole adult life span.
In the "discussion" section: Our results have shown that TRF-mediated benefits in IFM were abrogated upon KD of AdSL, Nmdmc, Ampkα, Ogdh, and SdhD (Supplementary Figure 7f, g and 8h). It is to note that AdSL, Nmdmc, Ampkα, Ogdh, and SdhD have been demonstrated to be important for muscle development using Mef2-Gal4 driver. Therefore, the abrogation of TRF benefits could be potentially due to the knockdown of genes essential for muscle function and not necessarily due to the absence of upregulation induced by TRF. In an attempt to rule out this possibility, we used DJ694-Gal4, an adult-muscle driver, to induce modest suppression of target gene expression. Interestingly, DJ694-driven KD of AdSL, Nmdmc, Ampkα and SdhD didn't lead to any significant decline in flight performance at the age of week 5 (Supplementary Figure 7f and 8h), suggesting the abrogation of TRF benefits was not caused by any muscle function decline from suppression of tested genes. Although the DJ694 driver expresses in the oenocytes and salivary glands during the developmental phase and abdominal muscle during the adult phase, the DJ694 driver allows modestly manipulating candidate genes in the adult IFM because of the absence of expression in muscle before the adult stage. However, potential effects on flight performance from DJ694-driven KD occurring outside of IFM cannot be assessed by our methods. Further assessment will also be needed to investigate the individual contribution of candidate genes in TRF-mediated benefits.
Reviewer #3 (Remarks to the Author): My remaining concerns have been satisfingly addressed and I congratulate the authors to a carefully revised and interesting study.
Response: Thank you. We greatly appreciated positive feedback from reviewer 3.
Reviewer #4 (Remarks to the Author): I have no further comments.
Response: Thank you very much for your evaluation and time.